An Examination of Food Neophobia in Older Adults
Bibliographic record
Abstract
Older adults are the fastest growing demographic worldwide and are also at increased nutritional risk. Food neophobia, the reluctance to eat and/or avoidance of novel foods, including functional foods, can be a contributing factor to nutritional risk and research in older adults is limited. The purpose of this study was to examine food neophobia and attitudes toward new foods in older adults. A total of 250 community-dwelling older adults (65+ years) were recruited to complete a researcher-administered validated questionnaire that explored attitudes towards new foods and food neophobia, which was assessed using the 10-question Food Neophobia Scale (Pliner & Hobden, 1992). Food neophobia scores were normally distributed with a mean ± SE of 29.6 ± 0.70. Participants were divided into three groups based on their food neophobia score: low (10 – 23; n = 81), medium (24 – 33; n = 88) and high (34 – 67; n = 81). Participants with a high degree of food neophobia were less willing to try new foods or food products (p < 0.001). Participant demographics (age, sex, ethnicity, education, annual household income) and health characteristics (number of prescription medications and health conditions) did not significantly differ among food neophobia groups. These data contribute to the limited literature on food neophobia in older adults and supports the need for further investigation into potential determinants of food neophobia (Supported by the OMAFRA-University of Guelph partnership).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".